{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "couldn't import doomish\n",
      "Couldn't import doom\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import gym\n",
    "import gym_ple\n",
    "import cv2\n",
    "import random\n",
    "import os\n",
    "\n",
    "def get_bird_point(cnts):\n",
    "    max_num = 0\n",
    "    res = (0, 0)\n",
    "    for cnt in cnts:\n",
    "        x = 0\n",
    "        y = 0\n",
    "        num = 0\n",
    "        max_x = 0\n",
    "        for point in cnt:\n",
    "            x += point[0][0]\n",
    "            y += point[0][1]\n",
    "            num += 1\n",
    "            if max_x < point[0][0]:\n",
    "                max_x = point[0][0]\n",
    "        if max_num < num and max_x < 120:\n",
    "            max_num = num\n",
    "            res = (int(x/num), int(y/num))\n",
    "    if((res[0]==0 and res[1]==0) or max_num < 20):\n",
    "        res = (100,350)\n",
    "    return res\n",
    "\n",
    "\n",
    "def cv_img2mat_img(img):\n",
    "    img2 = np.zeros(np.shape(img),dtype='uint8')\n",
    "    img2[:,:,0] = img[:,:,2]\n",
    "    img2[:,:,1] = img[:,:,1]\n",
    "    img2[:,:,2] = img[:,:,0]\n",
    "    return img2\n",
    "\n",
    "# 求灰度图像众数, uint8\n",
    "def gray_img_data_mode(img_gray):\n",
    "    w, h = np.shape(img_gray)\n",
    "    data = np.reshape(img_gray, w*h)\n",
    "    return np.argmax(np.bincount(data))\n",
    "\n",
    "def color_img_data_mode(img):\n",
    "    modes = []\n",
    "    for c in range(3):\n",
    "        img_c = img[:,:,c]\n",
    "        w, h = np.shape(img_c)\n",
    "        data = np.reshape(img_c, w*h)\n",
    "        modes.append(np.argmax(np.bincount(data)))\n",
    "    return modes\n",
    "\n",
    "def get_feature_points(img):\n",
    "    img = img[0:400, :, :]\n",
    "    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    mode = gray_img_data_mode(img_gray)\n",
    "    img_gray[img_gray[:,:] == mode] = 0\n",
    "\n",
    "\n",
    "    img_bit = np.zeros(img_gray.shape).astype('uint8')\n",
    "    img_bit[img_gray[:, :] > 0] = 1\n",
    "    \n",
    "    # 获取连通域\n",
    "    image, cnts, hierarchy = cv2.findContours(img_bit, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "    bird_point = get_bird_point(cnts)\n",
    "    bird_x = bird_point[0]\n",
    "    row_sum = np.sum(img_bit, 0)\n",
    "    obs_x = np.argmax(row_sum[bird_x:] > 200) + bird_x\n",
    "    col_val = img_bit[:,obs_x]\n",
    "    obs_y1 = np.argmax(col_val==0)\n",
    "    obs_y2 = len(col_val) - np.argmax(col_val[::-1]==0)\n",
    "    if(obs_y1==0 or obs_y2==400):\n",
    "        obs_x = 280\n",
    "        obs_y1=100\n",
    "        obs_y2=300\n",
    "    return np.array([bird_point[0],bird_point[1],obs_x,obs_y1,obs_x,obs_y2])\n",
    "\n",
    "def get_feature_distances(img):\n",
    "    b_x, b_y, o_x1, o_y1, o_x2, o_y2 = get_feature_points(img)\n",
    "    return np.array([o_x1-b_x,o_y1-b_y,o_y2-b_y])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving image from angent\n",
    "\n",
    "env = gym.make('FlappyBird-v0')\n",
    "env.seed(0)\n",
    "count = 0\n",
    "for i in range(10):\n",
    "    env.reset()\n",
    "    # while True:\n",
    "    for x in range(20):\n",
    "        action = 1\n",
    "        if random.random() < 0.01:\n",
    "            action = 0\n",
    "        img, _, done, _ = env.step(action)\n",
    "        \n",
    "        count += 1\n",
    "        bgr = np.zeros(img.shape)\n",
    "        bgr[:,:,0] = img[:,:,2]\n",
    "        bgr[:,:,1] = img[:,:,1]\n",
    "        bgr[:,:,2] = img[:,:,0]\n",
    "        # cv2.imshow('image', bgr)\n",
    "        # cv2.waitKey()\n",
    "        cv2.imwrite('pngs/%d.png' % count, bgr)\n",
    "        if done:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'os' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-4440f6c949ad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# loading image from pngs/*.png\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfiles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pngs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mB\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'os' is not defined"
     ]
    }
   ],
   "source": [
    "# loading image from pngs/*.png\n",
    "files = os.listdir('pngs')\n",
    "R = 2\n",
    "G = 1\n",
    "B = 0\n",
    "for f in files:\n",
    "    name = str(f)\n",
    "    if(name.endswith('.png')):\n",
    "        img = cv2.imread('pngs/%s' % f)\n",
    "        b_x, b_y, o_x1, o_y1, o_x2, o_y2 = get_feature_points(img)\n",
    "        plt.subplot(1, 4, 1)\n",
    "        plt.imshow(cv_img2mat_img(img))\n",
    "        plt.plot([b_x],[b_y],'xk')\n",
    "        plt.plot([o_x1],[o_y1],'og')\n",
    "        plt.plot([o_x2],[o_y2],'ob')\n",
    "        plt.show()\n",
    "        print(get_feature_distances(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 展示特征提取过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mjfEbCLmlFXqrbW/DjHolXnwUbvvNdKKtGvl/Ucho0z89g6/plqx/vhzKDRczIr0MOT+pp7e0Q46Eboq0iwZ6pQujvcijyEb2stjSDDmbhMqJrOBv3ZxI2NjMP2mNIep/6Ybw7XBCkottMMR2czbV2ur4aRa8NEPG+AnNoVl5o/Qh6WTegIkR/znR36uyDUun033WH5s5jQAnFi4BoGThhkPnXwN2At959jQd14ttDdx4WRD8gsf3W70J1ki/+IYPrH5shxQD2KDH+V9iuixeGTkKU7KQkEWm/7LHw3NdEfZWLHvLlmvGz3LNnrPpnjobjp3G74rQdbC3bHEI1+w5y9XjZ9ecWG5l/KWxl56b9P+S7reZu6VFTLYTnDKEpCWnAb++gVtyKkPPlpcsnEDH+d7gQZmsiNButDKx6YoXlvJYxT8f+oi/+BRcvcdngZERxPil123nBWdQ/DQ2WbnD4MRQrlWytgUB7n0SrtljMcbx4okzWASHpe2ERHx/8qDSwmbFj61fHGKWxAZoLzYR/GfThc3m8+cmw6T7baYBepN6unR6SLGG9KbHREue0+2QR5Itb3s724Z7noSXHfIlBsgJp5AJmhc1eGCm4jO/WcnaZw8f9FngyVaXQzWLMXBs0dEVuOcp34mRj29MbrlwEHoX9rP46mwF8LXobJk0vh4XGeFYswti2Vsx3PMUWCP84GU9wc+E+Rzx8RPka4pfMoYfuGxJfMguVADOwQOz1WXnJl44hmHXQKsfCa1vmiEPJ8aG7VPpz4YNgxcBKcPPlre9iUDHOe550vcRW+DGy8LVXUCc87f1DhIxtAWu2XMWa4RE4MFnd+PEZ5oHgxgD7K9Znph3ODFYTF/8Gw5BbKDdaPrMVaDjDE7gBZec5USry75KxLGmcKJxCaWc2B6oWUQsiYOucyG2V7nIGF4WYhtWj3+gGvFkY23xscJdR338lx/ydxOdRtNPBApZ/EHnJj3HOPHCn2ZS1vRWeGkv8lAiLk2P6ashiwl3jirKCuvdyyLqct3Eaazxt/OPzO6i1WhjDZSqZcDrxVdnq6FkJnzz2R2AL1/kpeR403GwZrOvjYHP/8yb+o73bz/+fpqNNvWxXmwfv+IzVkqA8PSC6XUh5P6wjzf8BWJvOeJA/Vkuq0XENs12Da3FKomFSm31+CcXoYtdU3xrJGsmbjaqfrFKeH16bjqrnhvxt7eAJP3nP+1RVoYQF/awWDqJJ4Zlf1jKSLDlJQsDPNPqcKgW0w2XeieybGYwEWFq7FlKJmF/LSZxcKThOLYwweJCk0QqHKja7H0/8J5/BcDPc4af5of4xH2fyWIdWfj7vthOYGpslkM1izXCsYaj6fxCihML49mFwAKHajGJ+JptZITYOmIDTzYc+6sRIj4rP1f8fRXLdxuypvgnml0EG+ILDult1Sn+3Fw3cYaTzc6yc5MyuD1KGLzOSxkKIoOIb2fMShTpnG0q1MpFz4ba3tJ66koTw35BiORqqCar1X631UCknL32ph++hU9yZmCcf3/DjwFw633v73vcGj8haMJoDEHchawGPEi7HLkxr/IBWBofIr+UeQ3xM1Z4zWrnJrwi28ui333ahE2HVJSHFWOXTFb3tb+pIiub0YcsIEZWFDgBHpzdyTV75jlYPc1TTb+p77Gm40M/8XYAfpofAuCT3MknuZP/N/0IV0xezSfu+4wXZQEZcAABHJaD1RiMcKh6mgef3eV7nYFjDZ+t7i0vndZeEuQc8R+c3ck143Prjx9EeVDsQefmbui1uEH/Prmh00Lbo7aGrfZKTK+jfV6JkK3S0+vs1rBS2WAjLWkXgnULcjbxn/3fgRMWFxcw+FsySTsEQmbnMFgjOBIcCYnrsq9i+0oTANzgM+ebfvK/9T38hpf9Mr/7uXeH4wuJJHSdYLO6sXC82ebSikXEZQK4PzfpttIP44xjcXEhfGtZmtb6SIYTzQ6XVi3OdUlIKPXFF7qShL2Z0zV2EeFqlYuVjj3BifM16+yZrn9RIhgjGJFl4iuYLV/RpdZc/vfVs+aSTbHmwrme8OZLFmnMLd6jZNiEaSmDxr/a2M9Vv80/X6RzsC5BbruY4wvj7KvO0XVCM+lwpjPPnzx1Dx3pUjIxrz10I40k4uj8bsoWxktzNJMOC67Jx4/eS0scCdCQH+XtP/pjffFvacAttR8aeOyO69Jwbdou4dPH78IaeO3l389ZJ9QSw18fv58ER0TMC/ZcD5Q52fIiu68Sh+Y4CzhEfJvdvGvikg5/+OTnAfiPl/1LDCU6kng/URE6LqGRtNlVbnLbE/fTFMc35iy/8E9u4JmW0JGEqYrh0ye+RARUjOUH972UljOUjGAQnHgBnk9a/N2Ze3jgbJcSlp+6/DBnujFn3RwfP/olgG01OR1k4uq3SPU7zSUF7vBYzWGmlfhWw6XCbFdxwClbv+e2NxPgvB1wxNgwD0D/KiC2PjteTZzuOPZwoQRpKecaOywX1I1Mpm0FF6DtTUhEcC6hJV3mJeLWo3dQm3R06SLE3Hr0Dr6n/m8oSYTD0pI28y7iI9+9j/JUExtywodm/oaJD1zPzC99lVsad2bHuKVxJ89713V9x33N2yZouBYffuJuFmlhJjo4A7cd+QKIpSuOsUkB4xAiPnLkHl73nBsZL0cYoOXadMSROIO10JEubYGPHvkSHdqUJtsA3Hr0DiLKvHLfv6DjSiQ4GtJhQZrc9sS9VCcTvG+J5Q+P3MtrLr+ePz36VZrSJZro+pY/LJ898WVee/lLqZgyiQFHh45L+IOjdxBPOJxJ6BJx25Ev4FzE380l1KdCS8U2ZVLn9kqEuc7mZcvb45XYG3/qlfjWqwZ7JebNBDKvxODkspHyhklLUfm5hfAGMbn5gk1mLYKwkrBt1vE2GnetYpYf/0bEOL0oFeEuYn0r9RC+55Jp2iJ87MiXWAhiNm8S/Nq2hGgi4sFn/ooyZX7+udfzR0e+ymnXxNGlIp3sj7E0AZBQvfVabnkDfaL8mrdN9B334AcO84Ejd3DWNahMQpsuCEQTCWBpnWqTNpdZOsQTMR9+4gtUTIXn73gl/3f+bl627zpakpAklvFywu1H7sdNtMAkNEzHn4xJEEm44+SXee1zrucTR7/KonRpu4TyZJcF6YKBxEBtAm47ci+VCYMhoUkXQTBiqI7DB5+4h+t2/TiHx1vc/uTdtKQD413mxZcmItPBTlgap/xUYzOMX9yAskS+b3ULPreRMbzjxTsGClN+i9S5TnA5aa3PlivlXMLpvQwZ6DmYXjCWZr8I7IwM//WalR1m3vbiHSTSs/5aSCQ7/u6SHbhyM28mUIvgN67dwS0Pz9NKYHID5Q0JbW/ZNqrpCdnCO5/nX70ITJzzdSkbzZbPpzywmdns+cRaSyYO6xPnLW97a9hZbp//HPMzXeJxQUxCG3KryhK6JLRZpI3hYwufw005SuGvTRw0Z0KtdEroAG7cIe9/Hr9pngdAZSqmLNCa7gKW+lSJ0/ZMEHyhLdCY9jFqUzGQUJ40NKY7VCYtjWkHdKhOxnRMk2/yl1CFO5O7iOcjFqcdHXHUJgyJSbNS/78WbQRoywIfnb8TJn2fcYRke200Z7pUJ2LmbROZgPlnulQmLa0ZR2UyRoywKIu0cDxU+QseXgCZdLisS8PHqE3EYBOqU76jwqVNx9aEyUTTWwSSy5a34vY2Dg4mg7wSDf4xK1CP4b9cVec3HlqAZH0ZbeaVuIrJ7FuvrgevRLtur8T0QpIXV0PQPGOIhMz6K++VeK4d+kwYeyUS9saGJF7ulbhaeSP1SjRhriXt1AGy75d67G0n6xXl9YrOubLRYWGrxr++kgVC07aIJ2D+lM8qq1OxF9nwN1aZsFSm/F9YxyZhW80gMlaoTsRev1MRnHbZbWtlyvbiTMU+eQgxDP7DC0ItFyMvUK1pH7QyGTaTMP6igTG+XxhHPOGXYs9Pt7NjNIPAVyYsrWlHdSomMb3j+vELzkBlIqY53aU6GU6d8cdNf2Yk/T72YycIQi5GdSKmEYQ9P3Yb4pmQoWZdFuGCZyxbsi/ypTVLNT6312A1WGu9/do6v/2tBc62wpRnegufXjiWTARaY9hbsfzqVWPsLK1sMlu2Phuf6wjve2yRsx1hZxDhnSUzMPv99Wt3hGOs/POl40+tv9509Y6+fZvOhTVQMoZfC8daKuIx/uJy07XetbyZCG972E8U33RNnd/Ez2Vk6z/SiV6b+/0W0HjgXLfwGxWlYRbj9Yx9y2vI6d+Ss9LL2EgzVU8+C65P2b73WmP8ex00T3WpTsXU9uWGINA45cW9ti/OBDEfA2Owkf/AN076EkJtUJzpdAz+8fQDmITjY3qJZ21vnI29OmV9BjvZf2pMWHEn9G4vjaF33DT7nfY/lwk2VUtjRGEM1Yk4OwfW+jgtIE3F+9qjcuWDrcim1rqPc2iaoRL5ibD5UBId7JVocl6Jhl+9aoxabFY1mbXhYlSL4VevHOvzSizZwdnvun/WLXpfdicR5mT3lf2L67GhWz+AsfMMLBWHwEXaNGo9JQhlZS6op15tMvZrGMIfVDbvZMU/F3RvkH60ZnqiZfPvRahO+ox7eQNaD5PGtT7bXDYGVh9DczYIriXLYAHE+l6M2oQfw9hUafWx598b+jiqk7G/zV7l3FnAhR/CmJ4QARgriOSyzTxOtr1h1eBrqr92dZ33BoPWXwkdCtakJQihlUDHCbc+tkjN4jPcNXolVoKv4q+vkJEWlXwmnh879K/x6buoiu9N1v3pFdiAIKeZLtHyD0mawaZONdljS947tre08nsjqA94ftk4jKG+tzTwGKuNwRqTCe2yMYQMViIYmxo8xrEBx8zeu8IxB43dmsE/53a2R60VawwV6zsQMIO9Ei1C2fp2snSCbK3D32gWWxQGjT+9uxikuybva6Zc1GzoBnhQ8rbW15zrvWuJfT6xzAqPrzXuZo1/xfOT1oUucHvUerAGYut7eOuxIba9rozU4soa/3gtNlTj83eCEehZf0lvT+nNOhsXIj6h0te34CdsVzvUVyBlIIXz1FPWz3a0R20GfYIGfRN6m2FtlZoeJLnYqZdhOh9Q5Pgm/0X+ompTTS7oL1bZMIWxcFI2TpHbo1azn3JOaDvh1PxZpufOMrtwhtn5M9zzpLe82mjsNH7bCY/OnGZ67ix3P+mYnT/D7OIZum5tNw1bGX8t1lz+Shti5W8XQqeQ1pBHD82QR4DtaI8S6c0XDowuftIys7gKBZfU3krCLf59TxlabgcWuG7iLMdaCS7bmH9pq1v++P6C0240Q4efpVwrZxO1LsRO5BKemvcLaY63OhyoxiTiF+OsNvG3YvzQqreW+LDCqQ8tie1msC4bcG7Azw0IA2rLkR/4qO1z/TsfeJbvvbbCjYfHssfuuX+Rrz/c4td+ac82juzCcUG7LJTNp1s/AGY+234zy4jT29xUPTYZIezpzODSQirGPa9E/7gNQpf2BDsRrhufy7wSD9UsJxfp7Rk9IP5gL0PBOkLaGHrYRbh2z1mM8ZmmDYvwEwGCz+Og0sI548va4qcdMWZJbOgJfSK9xTL5cxMvHINkZ/8JT79MM/AR28Xve6+t8KrXneDPP3wpNx4e4577F7PvLxa2vA/Z/WOTxZ/69roOoKydGPi9LwCcBU6u+Lrphc097mI34fNHhZcfhCh0TKTWVoJ4c9Y008Xw5ekK14zPYeabOODB2d28LHglnmp3MyeYYw1H2wmPzJzmdHNPX3wvzkHoU+svMXQdNGjBghe5Y03HicU9xAYiK8E0IN321PDIzLMcqsZM1ndmLYRxtvR79fgGeGqN8R89vZMbD/UWiRhDv9ALtJ3hgdnl5wZ8Jpx5JaY4P8tnrOnfanUEuPHwGH/+4Ut51etO8Is/u5sPfuxMJs4XC1ueIe+6IuaHPzWeff/t7zzW9/z3/LMX9n1/ruc3I8bFOIby6zvL3n8+dBLvbnLf06mXoXDjZaSKHG7F6XUf4DNBa4IhrN/FAwH2V3uF7v1VyxNzwsmF3ZRtf/wbDoU/vnzskC06JPNK7MXvkd9W9eTCbk4tGCqn/Su8V2JaOFg9/oHq2uPHVvjiU6lXohClsSU1xU1jLz834AdFrqoiAAAJ20lEQVSzdKGPd6KWnnfYiHHj4TF+8Wd38473PctNv7LnohJjuAAZcp6lArIZMc5X6C6WMVSrtfM+Zp5K1OUlk89igEQsD8/uprHYIjZQqlUArxf3z1S5evwsLx5vZNWTY80Ecb2l0sebjstChvzff+QWAD55+Gs+xm+fArwTzEs++z/ZUe/FThx+Jz8jxEY4WLUcbXS90DuQYAlj8Zl3gvcyPLRjlkO1iDhYXDkxNBeqRBZqY6vHl7CcZy3xo1A+cWJoLFaJjDfIBR/7K7NVrhk/M/DcQFj8kdtjmuxLH2TUasjga8Yf/NgZbvqVPXzwY2d4+eHaRSfK62VDgnwxZqXFGsNTy+KcLyeaXQ7VYsRPKw58jSAca3Yzr8RQJsUBzcUWDu+VmJIKMfcHr8Tw+Cfu+8wyr0Rfnuj2vAybLovdZ70FHMh5GRq8n2Fk4MlGwv5qxCDT0EHx91XsmuPnvRKXnxd/boyRzCsxf24gfJFfrpd/vwx4cMjJ14xvPDzGyw/X+r6/GLggk3prEZnzjbEZQncxjGEz2V+1/V6JyzUNY+Bg1RJHXm0enN1FV3xd44HZciZ0H/qJt/NJ7lx6iIyVvBIPVm3mZbivEvF/ZnZlKyrSLDeOvHVWgmVvxYtjKqj7q73NqQZdU5bGf3B2J11n1hR/f9X6/HnAeUnPjTXCgTG77NwA27KL33by9YdbfeKb1pS//nDrohHkjbAuQW42G33fb8bt/YXOSkdlDJvN8abjQK2/0Tm/kVL2ulZCbByXhkz4uvE5QHiq6XimcUn2utQnEdbulXi8lXCo5vPz463Exw5+jQ8+uwtY3aj2eNOxv5LLYM8RH+C6ybNrin88ZOz7K1EmyoO8Eo83lp+b1CvxQu/it50Mam278fCYivE5WJ+F01zC0S/OZ98f/eLXVn39uZ7fjBgX4xjOnppf5ZXrR4C91VLWTywitJI2uIS5uUUiY0EiEimztxxTjkJGCUjYAbslXRaTCo2kzTs+9T+4YvJqAB6ffgR+0mfLz/3m6zOx+9PfBL7wHzh5/UdIXEJHHJeU2iwmQkTMVNmS0ObpRotLK5ZWUqYrHWKxrLRccX/V4gS6LqGLY3HOt6NElHy5oRJl7720EvH0gvB0s8G+CiwmJRquibG+bNOVLl1xLLgmi0nEnpIhNqXgON67UIkIHenSSITFpMGesqGVQNslJLRJJEzAbsMufsr2suWTegfHL+Xdr3rzug6gbD7/+i/evanxOi7m4ZmdXDsxRyJCy3VZdM1lXomtpMLXZ/zG7lfvmaOVdGlKh48f/WLmlej4Uf745/pNajOvxLctP/a7/7ZLU7xX4keP3pN5JT7dFPZVDJ890fNKvHbieiJX9tqGw2HJeyUeazj2VixNASfLvRKf6rR5Ts13lBxtJLSSKnvKXW4/4r0SH5v3Xolt570Sm2L47Ml+r8TLqlWSMBWXeiU2XYtvzt3Dg/M9r8QFVw0+ksErseC7+Cmbjy4MUTZEIo7n7z5FMxH+8AlvzfVApU0ykVpzWW5d+BsaZ8qZNdeHnvDWXPee7VKZ7FlzfZNPMfGJF7EwIzTf8HBmzXVL485lBrYHP3CY93z3rzmbBGuuCb+H9W3znwMsC8fbVIPZgAU+On8nMh1n1lyfDtZci0mJGMvOUsKHnrif7niLxCR0J312euvi32AlwsxUlllzfa3cJdnj99V2xh+jMS1UJgzdTkJ7wltzNcTwmfZdNI9FA6252uM+RmTgo/OfY2HO8ZU5R3WqZ82FkayV0D8Y/m9Gr2QBG/e4G9bjbsY4VJAVFt1p/ujIvbzq8pfARBNLh7bp0lOMhMRbtdKlyUeO3kV9wlKhTSLQmOlk+0AnpkMTiMYt8vvP55Y39o4jv/886hM1YhPTmoHORJOELpF0OTOdj5GA9Ky5qlMxCdCVDh3p0JAOD85/mvpEzN+evI+qiXjN5dfz8SNfIZ4QWqaNMy4bf5sO0KE6Ltx25AvUJw2ROEokzJ9qgxFqUzGJCYtGJiwzp9pUJiOMCY4yAg1JqE9W+PrsX/HwPFQm/TjPTLcAshiYhPIUYRVgb1IP/ArM/s2R/TBHbWEIbN9G9kUQY9AMWdkocUJj6gy3L9y1qjVXeQoggcjSyOytvOOKpHsiBbFpBpGS9z8v2Fv5TLFrOiR0MHthPrdHcn1vjEuXES+5g2+eChZbk5bylMWYLtZ0aZg2ZgpawO3zd5GMQ+NUx1tzyXJrLpkSzCQsho3yDVDfF/mJPAetU6k1l6+Ot051qUxZKsFqqzXdpWs72L0dBGiFQdb3xr6rzQXHmIk4S4Sbp7qZNVfabNwnvumk3ogtnVY2hgqykmmFs0IlWFc1l3j+Nae7VCYsxtg+NxdDsOYyEraRdLRmvC8heIFtzfj3Qs/pJb9hvYHQNyaYTNihNdvNfA/TONXJMPuVvj9dnGH9ZFllMqZ1quvdZNKfJQhzerB8Lppac6UmtM1pX3qoTnnfRm+267woh9LJMmuxJTHSsYOP0wZIfJ+yEVkmvsIAp3Fl6NGShXLetIIfYmpxlZJaa4GjHsSq7yY7lEibwSQ27Row+Fv51bwSU1LPxVbOKzHr003j5LwSZYDnYjO12IJs/NXcRaY2GSMDNiFKv0/LJqlw16binldiOCeDxp4erzrZ80rMT+CZ2AzYWYnC73OtbBwtWSjnhcWXH/JegRmRF8GV7qwt4CzU98W97DXF9IxkV7NySrPt1Dh2qUksZuUxpMev7R0g+KF1OG/Gu+zYdoWLRSj5pudltW4IG7L82r4YG7b2zHCu55KevzWQ7M0rxlUuHlSQFSAtW5jVzWXD8yx5zVrea1MT1zWMw64ieiuNgZUeHxB3UHa82vi8ae25x7/aeRBjw/ZL9HVZpO4w6hiigDqGKDkGtciu9TXneu9aYp9PLLPC42uNu1njX/H8GF8r7ttmI9SThbUbwCrDw0YcQ1SQFeUCIOmu/vkyRXonkDmNKBc7KsiKcgEw1u8Y7TtDgiKHTYa8p56WLEYNndRTNsTMt56Z/uPLf+/Ido9jlPnLB9b0suds5jG/8UhrOtr/D/p7LQZr+t2qICuIyNR2j0HZfPT3OnxoyUJRFKUgqCAriqIUBBVkRVGUgqCCrCiKUhBUkBVFUQqCCrKiKEpBUEFWFEUpCCrIiqIoBUEFWVEUpSCoICuKohQEFWRFUZSCoIKsKIpSEFSQFUVRCoIKsqIoSkFQQVYURSkIKsiKoigFQQVZURSlIJj12I8bY04Bagmz/TxH3SAUZfRYlyAriqIoW4eWLBRFUQqCCrKiKEpBUEFWFEUpCCrIiqIoBUEFWVEUpSCoICuKohQEFWRFUZSCoIKsKIpSEFSQFUVRCsL/B7etDH5Xyq3cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ddn1Iwwi5hryPkFmaq31b03aXGln74HmEfEGrL1jWWiUi8XCwBYiLIm2L1LF9CLQL9rS2pu0uNfL0gREy8okGUp6lqYq2LVon+kFiGuG6HNPUNu8HUp4+lCDLCNamSNuiddqNYF2PaWqb9wMp79p6aQhkxNkCyBR0tvZ52/rog+34tm1JRdpG+1i0jme2ADIFna193rY++mA7vm1bUpG20T4WrZOGKYsaKnXKAtUpccoC1XKdsmDFEABQgkAGACUIZABQgkAGACW4ygIi78ig/EBOVN0NyCKfxT6TTwb3Bbv4verg9LslkCFBEFxcdR/gH7/X+mHKAgCUIJABQAkCGQCUIJABQAkCGQCUIJABQAkCGQCUIJABQAkCGQCUIJABQAkCGQCUIJABQAkCGQCUIJABQAkCGQCUIJABQAkCGQCUaAVB4L5zq/WxCEv9KLCI1SCA5skUyACA8jBlAQBKEMgAoASBDABKEMgAoASBDABKEMgAoASBDABKEMgAoASBDABK/B9AsMQ/EGCs/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# loading image from pngs/*.png\n",
    "files = os.listdir('pngs')\n",
    "\n",
    "count = 0\n",
    "for f in files:\n",
    "    name = str(f)\n",
    "    if(name.endswith('.png')):\n",
    "        raw_img = cv2.imread('pngs/%s' % f)\n",
    "        img = raw_img[0:400, :, :]\n",
    "        img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "        mode = gray_img_data_mode(img_gray)\n",
    "        img_gray[img_gray[:,:] == mode] = 0\n",
    "\n",
    "\n",
    "        img_bit = np.zeros(img_gray.shape).astype('uint8')\n",
    "        img_bit[img_gray[:, :] > 0] = 1\n",
    "        \n",
    "        b_x, b_y, o_x1, o_y1, o_x2, o_y2 = get_feature_points(img)\n",
    "        \n",
    "        plt.subplot(1, 3, 1)\n",
    "        plt.imshow(cv_img2mat_img(raw_img))\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.subplot(1, 3, 2)\n",
    "        plt.imshow(cv_img2mat_img(img))\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.subplot(1, 3, 3)\n",
    "        plt.imshow(img_bit)\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.plot([b_x],[b_y],'xk')\n",
    "        plt.plot([o_x1],[o_y1],'og')\n",
    "        plt.plot([o_x2],[o_y2],'ob')\n",
    "        plt.show()\n",
    "        count += 1\n",
    "        if count==10:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
